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25 Aug 2011

Sample size in Quantitative studies

Is your sample size sufficient for your quantitative analysis? This is a question that a lot of doctoral researchers ask. I guess it depends on the type of analysis used. My PhD research looked at blog readers and I used Structural Equation Modelling (SEM) to analyse my data. I used the following to justify my sample size.

For factor analysis, according to Hair et al., (2010), a sample should preferably be more than 100 for factor analysis to proceed

However, according to Tabachnick and Fidell (2007 p. 613), it should be higher than 300 cases, the number that is considered “comfortable”.

If you are doing SEM:

A ratio of ten responses per free parameters is required to obtain trustworthy estimates (Bentler and Chou, 1987).

Others suggest a rule of thumb of ten subjects per item in scale development, is prudent (Flynn and Pearcy 2001).

However, if data is found to violate multivariate normality assumptions, the number of respondents per estimated parameter increases to 15 (Bentler and Chou 1987; Hair, et al. 2006).

4 comments:

Visualizing data with graphics can be more precise and revealing than conventional statistics. If you do not use statistical graphics, then you forfeit a deeper understanding of a dataset's structure. quantitative research data analysis

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About Dilip

Dilip is Associate Professor of Marketing with the Nottingham University Business School in The University of Nottingham Malaysia Campus. His research interests include digital consumption, social networking, strategic marketing and intercultural marketing.

An avid blogger, Dilip has also advised various organisations on their search engine optimisation and social media marketing campaigns. His research work has been published within a range of publications – both in print and online.